# Copyright Lightning AI.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import signal
from copy import deepcopy
from typing import Any, Callable, Dict, Optional, Type, Union

from packaging.version import Version

import lightning.pytorch as pl
from lightning.fabric.utilities.device_dtype_mixin import _DeviceDtypeModuleMixin
from lightning.pytorch.callbacks import Checkpoint, EarlyStopping
from lightning.pytorch.strategies.launchers import _SubprocessScriptLauncher
from lightning.pytorch.trainer.connectors.signal_connector import _get_sigkill_signal
from lightning.pytorch.trainer.states import TrainerStatus
from lightning.pytorch.utilities.exceptions import _TunerExitException
from lightning.pytorch.utilities.model_helpers import is_overridden
from lightning.pytorch.utilities.rank_zero import rank_zero_info, rank_zero_warn

log = logging.getLogger(__name__)


def _call_and_handle_interrupt(trainer: "pl.Trainer", trainer_fn: Callable, *args: Any, **kwargs: Any) -> Any:
    r"""Error handling, intended to be used only for main trainer function entry points (fit, validate, test, predict)
    as all errors should funnel through them.

    Args:
        trainer_fn: one of (fit, validate, test, predict)
        *args: positional arguments to be passed to the `trainer_fn`
        **kwargs: keyword arguments to be passed to `trainer_fn`

    """
    try:
        if trainer.strategy.launcher is not None:
            return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
        return trainer_fn(*args, **kwargs)

    except _TunerExitException:
        _call_teardown_hook(trainer)
        trainer._teardown()
        trainer.state.status = TrainerStatus.FINISHED
        trainer.state.stage = None

    except KeyboardInterrupt as exception:
        rank_zero_info("\nDetected KeyboardInterrupt, attempting graceful shutdown ...")
        # user could press Ctrl+C many times, disable KeyboardInterrupt for shutdown
        signal.signal(signal.SIGINT, signal.SIG_IGN)
        _interrupt(trainer, exception)
        trainer._teardown()
        launcher = trainer.strategy.launcher
        if isinstance(launcher, _SubprocessScriptLauncher):
            launcher.kill(_get_sigkill_signal())
        exit(1)

    except BaseException as exception:
        _interrupt(trainer, exception)
        trainer._teardown()
        # teardown might access the stage so we reset it after
        trainer.state.stage = None
        raise


def _interrupt(trainer: "pl.Trainer", exception: BaseException) -> None:
    trainer.state.status = TrainerStatus.INTERRUPTED
    _call_callback_hooks(trainer, "on_exception", exception)
    if trainer.datamodule is not None:
        _call_lightning_datamodule_hook(trainer, "on_exception", exception)
    trainer.strategy.on_exception(exception)
    for logger in trainer.loggers:
        logger.finalize("failed")


def _call_setup_hook(trainer: "pl.Trainer") -> None:
    assert trainer.state.fn is not None
    fn = trainer.state.fn

    # It is too early to move the model to the device, but we fake the `LightningModule.device` property
    # so the user can access it in the `LightningModule.setup` hook
    for module in trainer.lightning_module.modules():
        if isinstance(module, _DeviceDtypeModuleMixin):
            module._device = trainer.strategy.root_device

    # Trigger lazy creation of experiment in loggers so loggers have their metadata available
    for logger in trainer.loggers:
        if hasattr(logger, "experiment"):
            _ = logger.experiment

    trainer.strategy.barrier("pre_setup")

    if trainer.datamodule is not None:
        _call_lightning_datamodule_hook(trainer, "setup", stage=fn)
    _call_callback_hooks(trainer, "setup", stage=fn)
    _call_lightning_module_hook(trainer, "setup", stage=fn)

    trainer.strategy.barrier("post_setup")


def _call_configure_model(trainer: "pl.Trainer") -> None:
    # legacy hook
    if is_overridden("configure_sharded_model", trainer.lightning_module):
        with trainer.strategy.model_sharded_context():
            _call_lightning_module_hook(trainer, "configure_sharded_model")

    # we don't normally check for this before calling the hook. it is done here to avoid instantiating the context
    # managers
    if is_overridden("configure_model", trainer.lightning_module):
        with trainer.strategy.tensor_init_context(), trainer.strategy.model_sharded_context(), trainer.precision_plugin.module_init_context():  # noqa: E501
            _call_lightning_module_hook(trainer, "configure_model")


def _call_teardown_hook(trainer: "pl.Trainer") -> None:
    assert trainer.state.fn is not None
    fn = trainer.state.fn

    if trainer.datamodule is not None:
        _call_lightning_datamodule_hook(trainer, "teardown", stage=fn)

    _call_callback_hooks(trainer, "teardown", stage=fn)
    _call_lightning_module_hook(trainer, "teardown", stage=fn)

    trainer.lightning_module._current_fx_name = None
    # these could have become stale if metrics are defined in `setup`
    trainer.lightning_module._metric_attributes = None

    # todo: TPU 8 cores hangs in flush with TensorBoard. Might do for all loggers.
    # It might be related to xla tensors blocked when moving the cpu kill loggers.
    for logger in trainer.loggers:
        logger.finalize("success")

    # summarize profile results
    trainer.profiler.describe()


def _call_lightning_module_hook(
    trainer: "pl.Trainer",
    hook_name: str,
    *args: Any,
    pl_module: Optional["pl.LightningModule"] = None,
    **kwargs: Any,
) -> Any:
    log.debug(f"{trainer.__class__.__name__}: calling lightning module hook: {hook_name}")

    pl_module = pl_module or trainer.lightning_module

    if pl_module is None:
        raise TypeError("No `LightningModule` is available to call hooks on.")

    fn = getattr(pl_module, hook_name)
    if not callable(fn):
        return None

    prev_fx_name = pl_module._current_fx_name
    pl_module._current_fx_name = hook_name

    with trainer.profiler.profile(f"[LightningModule]{pl_module.__class__.__name__}.{hook_name}"):
        output = fn(*args, **kwargs)

    # restore current_fx when nested context
    pl_module._current_fx_name = prev_fx_name

    return output


def _call_lightning_datamodule_hook(
    trainer: "pl.Trainer",
    hook_name: str,
    *args: Any,
    **kwargs: Any,
) -> Any:
    log.debug(f"{trainer.__class__.__name__}: calling lightning datamodule hook: {hook_name}")

    if trainer.datamodule is None:
        raise TypeError("No `LightningDataModule` is available to call hooks on.")

    fn = getattr(trainer.datamodule, hook_name)
    if callable(fn):
        with trainer.profiler.profile(f"[LightningDataModule]{trainer.datamodule.__class__.__name__}.{hook_name}"):
            return fn(*args, **kwargs)
    return None


def _call_callback_hooks(
    trainer: "pl.Trainer",
    hook_name: str,
    *args: Any,
    monitoring_callbacks: Optional[bool] = None,
    **kwargs: Any,
) -> None:
    log.debug(f"{trainer.__class__.__name__}: calling callback hook: {hook_name}")

    pl_module = trainer.lightning_module
    if pl_module:
        prev_fx_name = pl_module._current_fx_name
        pl_module._current_fx_name = hook_name

    callbacks = trainer.callbacks
    if monitoring_callbacks is True:
        # the list of "monitoring callbacks" is hard-coded to these two. we could add an API to define this
        callbacks = [cb for cb in callbacks if isinstance(cb, (EarlyStopping, Checkpoint))]
    elif monitoring_callbacks is False:
        callbacks = [cb for cb in callbacks if not isinstance(cb, (EarlyStopping, Checkpoint))]

    for callback in callbacks:
        fn = getattr(callback, hook_name)
        if callable(fn):
            with trainer.profiler.profile(f"[Callback]{callback.state_key}.{hook_name}"):
                fn(trainer, trainer.lightning_module, *args, **kwargs)

    if pl_module:
        # restore current_fx when nested context
        pl_module._current_fx_name = prev_fx_name


def _call_callbacks_state_dict(trainer: "pl.Trainer") -> Dict[str, dict]:
    """Called when saving a model checkpoint, calls and returns every callback's `state_dict`, keyed by
    `Callback.state_key`."""
    callback_state_dicts = {}
    for callback in trainer.callbacks:
        state_dict = callback.state_dict()
        if state_dict:
            callback_state_dicts[callback.state_key] = state_dict
    return callback_state_dicts


def _call_callbacks_on_save_checkpoint(trainer: "pl.Trainer", checkpoint: Dict[str, Any]) -> None:
    """Called when saving a model checkpoint, calls every callback's `on_save_checkpoint` hook."""
    pl_module = trainer.lightning_module
    if pl_module:
        prev_fx_name = pl_module._current_fx_name
        pl_module._current_fx_name = "on_save_checkpoint"

    for callback in trainer.callbacks:
        with trainer.profiler.profile(f"[Callback]{callback.state_key}.on_save_checkpoint"):
            callback.on_save_checkpoint(trainer, trainer.lightning_module, checkpoint)

    if pl_module:
        # restore current_fx when nested context
        pl_module._current_fx_name = prev_fx_name


def _call_callbacks_on_load_checkpoint(trainer: "pl.Trainer", checkpoint: Dict[str, Any]) -> None:
    """Called when loading a model checkpoint.

    Calls every callback's `on_load_checkpoint` hook. We have a dedicated function for this rather than using
    `_call_callback_hooks` because we have special logic for getting callback_states.

    """
    pl_module = trainer.lightning_module
    if pl_module:
        prev_fx_name = pl_module._current_fx_name
        pl_module._current_fx_name = "on_load_checkpoint"

    callback_states: Optional[Dict[Union[Type, str], Dict]] = checkpoint.get("callbacks")

    if callback_states is None:
        return

    is_legacy_ckpt = Version(checkpoint["pytorch-lightning_version"]) < Version("1.5.0dev")
    current_callbacks_keys = {cb._legacy_state_key if is_legacy_ckpt else cb.state_key for cb in trainer.callbacks}
    difference = callback_states.keys() - current_callbacks_keys
    if difference:
        rank_zero_warn(
            "Be aware that when using `ckpt_path`,"
            " callbacks used to create the checkpoint need to be provided during `Trainer` instantiation."
            f" Please add the following callbacks: {list(difference)}.",
        )

    for callback in trainer.callbacks:
        with trainer.profiler.profile(f"[Callback]{callback.state_key}.on_load_checkpoint"):
            callback.on_load_checkpoint(trainer, trainer.lightning_module, checkpoint)

    if pl_module:
        # restore current_fx when nested context
        pl_module._current_fx_name = prev_fx_name


def _call_callbacks_load_state_dict(trainer: "pl.Trainer", checkpoint: Dict[str, Any]) -> None:
    """Called when loading a model checkpoint, calls every callback's `load_state_dict`."""
    callback_states: Optional[Dict[Union[Type, str], Dict]] = checkpoint.get("callbacks")

    if callback_states is None:
        return

    for callback in trainer.callbacks:
        state = callback_states.get(callback.state_key, callback_states.get(callback._legacy_state_key))
        if state:
            state = deepcopy(state)
            callback.load_state_dict(state)


def _call_strategy_hook(
    trainer: "pl.Trainer",
    hook_name: str,
    *args: Any,
    **kwargs: Any,
) -> Any:
    log.debug(f"{trainer.__class__.__name__}: calling strategy hook: {hook_name}")

    pl_module = trainer.lightning_module
    prev_fx_name = pl_module._current_fx_name
    pl_module._current_fx_name = hook_name

    fn = getattr(trainer.strategy, hook_name)
    if not callable(fn):
        return None

    with trainer.profiler.profile(f"[Strategy]{trainer.strategy.__class__.__name__}.{hook_name}"):
        output = fn(*args, **kwargs)

    # restore current_fx when nested context
    pl_module._current_fx_name = prev_fx_name

    return output
